The old adage, "An idle mind is the devil's playground" is being challenged by neuroscientists who are reporting that not only is the "idle mind" busy, but it may be busy replaying previous activity or consolidating recently learned material into long term memories. The brain idles in a default mode network (DMN) in which slowly changing activity is temporally correlated between large areas of the brain. The DMN denotes a state in which the subject is awake and alert, but not involved in a goal directed task. It can be thought of as a searchlight, scanning the internal and external environment, or as a source of stimulus-independent, self-referential thought. When necessary, the brain can switch out of the DMN and into the task mode network (TMN). The TMN denotes a state of attention focused on performing a task. It can be thought of as a spotlight, suppressing everything not necessary for performing the task at hand. Characteristic symptoms of schizophrenia may be associated disturbances in the idling brain, including difficulties switching out of idle and into gear when tasks demand it and auditory verbal hallucinations. Using resting state activity as our conceptual starting point, we propose to use EEG+fMRI data to study (1) the interaction between resting state activity and task performance, (2) the responsiveness of auditory and visual cortex to probes during resting state activity, and (3) the effect of self-initiated actions on resting state activity, all with the goal of elucidating the pathophysiology of schizophrenia. To address each, EEG + fMRI will be collected simultaneously from 70 patients with schizophrenia and 70 age- and gender-matched controls during three different experiments. The first involves performing a picture-word matching task, the second involves presentation of tone and checkerboard probes during a resting scan, and the third involves talking. (1) Our pilot data suggest that fluctuations in EEG power reflect engagement and disengagement from DMN. We predict that failures to switch out of the DMN and engage the TMN will contribute to poor task performance characteristic of patients. (2) Rest is an ideal state for auditory verbal hallucinations (AVH) as they often occur during rest. Our pilot data suggest that during rest, DMN recruits auditory cortex, especially in patients who hallucinate, and may make auditory cortex relatively inaccessible to external sounds. We predict that processing of tones and checkerboards will be compromised during the peak of DMN activity in all subjects, with tone processing being more affected in hallucinating patients, because of their recruitment of auditory cortex into the DMN. (3) During talking, auditory cortex is disengaged from the DMN, perhaps making it both less responsive to the acoustic experience of our internal dialog and to the sounds of our own speech. This may be due to the successful action of the corollary discharge mechanism, which is dysfunctional in patients. We predict a break down in this pattern in schizophrenia. Each aim addresses the contribution of abnormal resting state activity to the pathophysiology of schizophrenia. PUBLIC HEALTH RELEVANCE: The VA provides health care to about 200,000 veterans with psychosis. Of these, about half have schizophrenia. The VA spends 15% of its total health care budget on medical and psychiatric care for this population. We propose novel brain imaging studies focused on understanding the idle mind, mind wandering, or the default mode. Idle time gives people the opportunity to relive and consolidate new information and get ready for the next hurdle. But idle time is used differently in patients with schizophrenia: Our preliminary data suggest the idle mind may be fertile territory for auditory verbal hallucinations, a symptom of schizophrenia associated with high mortality and morbidity. Our preliminary data also suggest that deficits in switching out of "idle" and into "gear" may result in poor performance on cognitive tasks, another feature of schizophrenia associated with poor outcome. Brain imaging data can help us understand the pathophysiology of schizophrenia and guide the search for targets for new treatments and development of outcome measures.